Abstract

An advantage of randomization tests for small samples is that an exact P-value can be computed under an additive model. a disadvantage with very small sample sizes is that the resulting discrete distribution for P-values can make it mathematically impossible for a P-value to attain a particular degree of significance. We investigate a distribution of P-values that arises when several thousand randomization tests are conducted simultaneously using small samples, a situation that arises with microarray gene expression data. We show that the distribution yields valuable information regarding groups of genes that are differentially expressed between two groups: A treatment group and a control group. This distribution helps to categorize genes with varying degrees of overlap of genetic expression values between the two groups, and it helps to quantify the degree of overlap by using the P-value from a randomization test. Moreover, a statistical test is available that compares the actual distribution of P-values with an expected distribution if there are no genes that are differentially expressed. We demonstrate the method and illustrate the results by using a microarray data set involving a cell line for rheumatoid arthritis. a small simulation study evaluates the effect that correlated gene expression levels could have on results from the analysis.

Department(s)

Mathematics and Statistics

Keywords and Phrases

Additivity; Microarray; Nonparametric test; Permutation; Randomization

International Standard Serial Number (ISSN)

0035-9254

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Royal Statistical Society; Oxford University Press, All rights reserved.

Publication Date

01 Aug 2003

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